Conventional consumer behavior modelling practices have been used in retail sales to identify and implement advertising strategies that drive consumers' purchases. The relatively new area of on-line commerce, also known as Internet e-commerce, has become a well accepted mechanism for consumers, i.e., individuals who are considering the purchase of a good or service, to become customers by purchasing products from various sources through the Internet. This type of on-line commerce enables consumers to decide when they search for products, how they search for products, and how and where they buy those products without the inconvenience of physically visiting numerous different sellers' real-world locations to comparison shop.
In the on-line environment, consumer behavior modelling practices routinely integrate consumer data with behavioral metrics and demographic information from third-party providers. With this data, advertisers and advertising channel providers can define and select populations of consumers, use data mining to build predictive models, and score consumers and consumer demographic groups based on relevant criteria. As a result of such predictive modeling, advertisers aim to determine which consumers are most likely to purchase products, services, take a desired action (e.g. a registration/sign-up) or respond to specific advertisements, which consumer segments maximize Return On Investment (ROI) on advertising campaigns and which consumers are at risk of attrition. Moreover, predictive models are conventionally used for consumer profiling and consumer modelling to predict consumer responses to marketing and sales campaigns, identify cross-selling and up-selling opportunities, manage consumer attrition and perform consumer valuations.
As a result of on-line commerce, sellers may increase their number of on-line transactions by, for example, increasing their visibility to visitors through advertising. This is often performed by purchasing sponsored searches, or paid searches, which are a type of contextual advertising where web-site owners pay an advertising fee, usually based on click-throughs or advertisement views to have their web-site search results shown in top placement on search engine result pages.
Sellers may also increase their number of on-line transactions by, for example, improving the shopping and purchase experience for consumers in their on-line environment, e.g., their on-line storefront or market place. Additionally, sellers may also increase their number of on-line transactions by increasing the actual or perceived benefit or value to the buyer through Transaction Related Offerings (TROs) such as price modifications, free shipping, bonded transactions, warranties, coupons, etc.
However, conducting conventional buyer behavior modelling practices in e-commerce requires measuring transaction volumes in a verifiable, consistent and reliable (and scalable) way and evaluating a measured transaction volume and associated data when various TROs are implemented to determine those the impact of the TROs on buyer behavior. For example, determining the impact of offering bonding on the purchase of an expensive piece of electronics equipment or the impact of free shipping for lawn furniture purchased on-line requires a comparison of the volume of on-line purchases of those products with and without their associated TROs.
However, a significant limitation of such modeling is that, conventionally, transaction volume is not measured in a verifiable, consistent, reliable and scalable way. Rather, advertisers, advertising channel providers and sellers routinely equate the number of “clicks” occurring at a web-site with the degree of interest that a buyer has and, rather inappropriately, the likelihood that a buyer will become a satisfied customer. Thus, the success of a web-site is conventionally measured based on the number of unique visitors, hits, click-throughs, or page views. However, on-line commerce suffers from the problem that it is difficult to determine whether, for example, the number of clicks in fact may be used to consistently and reliably predict the likelihood that a buyer will enter into an on-line transaction.
Another conventionally known option for tracking visitor behavior is the tracking of keywords input to search engines to determine what is of interest to visitors of a particular web-site. For example, if a visitor types in the keywords “rear projection HDTV,” it may be assumed that the visitor is looking to learn more about or purchase such a product. Therefore, search engine providers have access to a great deal of information regarding visitors' interests. Accordingly, search engine providers can provide data indicating what on-line buyers search on and where those buyers go based on search results. In turn, a particular seller can track what buyers do when they get to the seller's on-line environment, what their purchase rates and repeat business percentages are, etc.
However, access to such information is limited because search engines and particular sellers are not privy to buyers' activities using other search engines or while visiting other sellers' on-line environments; moreover, the period of time during which a buyer's click-stream (i.e., the virtual trail that a visitor leaves behind while surfing the Internet) or key words can be tracked is limited to that period prior to and including conversion (e.g., when a consumer becomes a customer as a result of a purchase of a product on-line).